import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
_lowerCAmelCase       =		[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					for attribute in key.split('''.'''						):
										if is_finetuned:
															if attribute in ["quantizer", "project_q", "project_hid"]:
																				# those layers are only relevant for pretraining and should be dropped
																				return

															if attribute == "ctc_proj":
																				# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																				_UpperCamelCase			     =     '''lm_head'''

										_UpperCamelCase			     =     getattr(__snake_case  ,     __snake_case						)

					if weight_type is not None:
										_UpperCamelCase			     =     getattr(__snake_case  ,     __snake_case						).shape
					else:
										_UpperCamelCase			     =     hf_pointer.shape

					assert hf_shape == value.shape, (
					    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
					    f""" {value.shape} for {full_name}"""
					)

					if weight_type == "weight":
										_UpperCamelCase			     =     value
					elif weight_type == "weight_g":
										_UpperCamelCase			     =     value
					elif weight_type == "weight_v":
										_UpperCamelCase			     =     value
					elif weight_type == "bias":
										_UpperCamelCase			     =     value
					else:
										_UpperCamelCase			     =     value

					logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""						)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     []
					_UpperCamelCase			     =     fairseq_model.state_dict()

					_UpperCamelCase			     =     hf_model.unispeech.feature_extractor

					for name, value in fairseq_dict.items():
										_UpperCamelCase			     =     False
										if "conv_layers" in name:
															load_conv_layer(
															    __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     hf_model.config.feat_extract_norm == '''group'''  ,     )
															_UpperCamelCase			     =     True
										else:
															for key, mapped_key in MAPPING.items():
																				_UpperCamelCase			     =     '''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																				if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
																									_UpperCamelCase			     =     True
																									if "*" in mapped_key:
																														_UpperCamelCase			     =     name.split(__snake_case						)[0].split('''.'''						)[-2]
																														_UpperCamelCase			     =     mapped_key.replace('''*'''  ,     __snake_case						)
																									if "weight_g" in name:
																														_UpperCamelCase			     =     '''weight_g'''
																									elif "weight_v" in name:
																														_UpperCamelCase			     =     '''weight_v'''
																									elif "bias" in name:
																														_UpperCamelCase			     =     '''bias'''
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														_UpperCamelCase			     =     '''weight'''
																									else:
																														_UpperCamelCase			     =     None
																									set_recursively(__snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						)
																				continue
										if not is_used:
															unused_weights.append(__snake_case						)

					logger.warning(f"""Unused weights: {unused_weights}"""						)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     full_name.split('''conv_layers.'''						)[-1]
					_UpperCamelCase			     =     name.split('''.'''						)
					_UpperCamelCase			     =     int(items[0]						)
					_UpperCamelCase			     =     int(items[1]						)

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
															)
															_UpperCamelCase			     =     value
															logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
															)
															_UpperCamelCase			     =     value
															logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
															    " found."
															)
															_UpperCamelCase			     =     value
															logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
															)
															_UpperCamelCase			     =     value
															logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
					else:
										unused_weights.append(__snake_case						)
@torch.no_grad()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case=None  ,     __snake_case=None  ,     __snake_case=True						):
					if config_path is not None:
										_UpperCamelCase			     =     UniSpeechConfig.from_pretrained(__snake_case						)
					else:
										_UpperCamelCase			     =     UniSpeechConfig()

					if is_finetuned:
										if dict_path:
															_UpperCamelCase			     =     Dictionary.load_from_json(__snake_case						)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_UpperCamelCase			     =     target_dict.pad_index
															_UpperCamelCase			     =     target_dict.bos_index
															_UpperCamelCase			     =     target_dict.eos_index
															_UpperCamelCase			     =     len(target_dict.symbols						)
															_UpperCamelCase			     =     os.path.join(__snake_case  ,     '''vocab.json'''						)
															if not os.path.isdir(__snake_case						):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case						)						)
																				return
															os.makedirs(__snake_case  ,     exist_ok=__snake_case						)
															_UpperCamelCase			     =     target_dict.indices

															# fairseq has the <pad> and <s> switched
															_UpperCamelCase			     =     42
															_UpperCamelCase			     =     43
															with open(__snake_case  ,     '''w'''  ,     encoding='''utf-8'''						) as vocab_handle:
																				json.dump(__snake_case  ,     __snake_case						)
															_UpperCamelCase			     =     WavaVecaPhonemeCTCTokenizer(
															    __snake_case  ,     unk_token=target_dict.unk_word  ,     pad_token=target_dict.pad_word  ,     bos_token=target_dict.bos_word  ,     eos_token=target_dict.eos_word  ,     word_delimiter_token='''|'''  ,     do_lower_case=__snake_case  ,     )
															_UpperCamelCase			     =     True if config.feat_extract_norm == '''layer''' else False
															_UpperCamelCase			     =     WavaVecaFeatureExtractor(
															    feature_size=1  ,     sampling_rate=16000  ,     padding_value=0  ,     do_normalize=__snake_case  ,     return_attention_mask=__snake_case  ,     )
															_UpperCamelCase			     =     WavaVecaProcessor(feature_extractor=__snake_case  ,     tokenizer=__snake_case						)
															processor.save_pretrained(__snake_case						)

										_UpperCamelCase			     =     UniSpeechForCTC(__snake_case						)
					else:
										_UpperCamelCase			     =     UniSpeechForPreTraining(__snake_case						)

					if is_finetuned:
										_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]  ,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						), '''w2v_path''': checkpoint_path}						)
					else:
										_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

					_UpperCamelCase			     =     model[0].eval()

					recursively_load_weights(__snake_case  ,     __snake_case  ,     __snake_case						)

					hf_unispeech.save_pretrained(__snake_case						)


if __name__ == "__main__":
					_lowerCAmelCase       =		argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
					)
					_lowerCAmelCase       =		parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)


"""simple docstring"""


from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



@add_end_docstrings(_lowerCAmelCase						)
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   **UpperCAmelCase_			:				List[str]      )->	Tuple:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )
              requires_backends(self							,   "vision"      )
              requires_backends(self							,   "torch"      )

              if self.framework != "pt":
                     raise ValueError(f"The {self.__class__} is only available in PyTorch."      )

              self.check_model_type(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   **UpperCAmelCase_			:				int      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		{}
              # preprocess args
              if "points_per_batch" in kwargs:
                     UpperCamelCase       			=		kwargs["points_per_batch"]
              if "points_per_crop" in kwargs:
                     UpperCamelCase       			=		kwargs["points_per_crop"]
              if "crops_n_layers" in kwargs:
                     UpperCamelCase       			=		kwargs["crops_n_layers"]
              if "crop_overlap_ratio" in kwargs:
                     UpperCamelCase       			=		kwargs["crop_overlap_ratio"]
              if "crop_n_points_downscale_factor" in kwargs:
                     UpperCamelCase       			=		kwargs["crop_n_points_downscale_factor"]
              # postprocess args
              if "pred_iou_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["pred_iou_thresh"]
              if "stability_score_offset" in kwargs:
                     UpperCamelCase       			=		kwargs["stability_score_offset"]
              if "mask_threshold" in kwargs:
                     UpperCamelCase       			=		kwargs["mask_threshold"]
              if "stability_score_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["stability_score_thresh"]
              if "crops_nms_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["crops_nms_thresh"]
              if "output_rle_mask" in kwargs:
                     UpperCamelCase       			=		kwargs["output_rle_mask"]
              if "output_bboxes_mask" in kwargs:
                     UpperCamelCase       			=		kwargs["output_bboxes_mask"]
              return preprocess_kwargs, forward_params, postprocess_kwargs






       def __call__(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]							,   *UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				Tuple=None							,   **UpperCAmelCase_			:				str      )->	Any:







              """simple docstring"""
              return super().__call__(UpperCAmelCase_							,   *UpperCAmelCase_							,   num_workers=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				str=64							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				float = 512 / 1_500							,   UpperCAmelCase_			:				Optional[int] = 32							,   UpperCAmelCase_			:				Optional[int] = 1							,   )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		load_image(UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor.size["longest_edge"]
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.generate_crop_boxes(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor(images=UpperCAmelCase_							,   return_tensors="pt"      )

              with self.device_placement():
                     if self.framework == "pt":
                            UpperCamelCase       			=		self.get_inference_context()
                            with inference_context():
                                   UpperCamelCase       			=		self._ensure_tensor_on_device(UpperCAmelCase_							,   device=self.device      )
                                   UpperCamelCase       			=		self.model.get_image_embeddings(model_inputs.pop("pixel_values"      )      )
                                   UpperCamelCase       			=		image_embeddings

              UpperCamelCase       			=		grid_points.shape[1]
              UpperCamelCase       			=		points_per_batch if points_per_batch is not None else n_points

              if points_per_batch <= 0:
                     raise ValueError(
                         "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
                         "To return all points at once, set points_per_batch to None"      )

              for i in range(0							,   UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		grid_points[:, i : i + points_per_batch, :, :]
                     UpperCamelCase       			=		input_labels[:, i : i + points_per_batch]
                     UpperCamelCase       			=		i == n_points - points_per_batch
                     yield {
                         "input_points": batched_points,
                         "input_labels": labels,
                         "input_boxes": crop_boxes,
                         "is_last": is_last,
                         **model_inputs,
                     }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				int=0.88							,   UpperCAmelCase_			:				Optional[int]=0.95							,   UpperCAmelCase_			:				Dict=0							,   UpperCAmelCase_			:				int=1							,   )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		model_inputs.pop("input_boxes"      )
              UpperCamelCase       			=		model_inputs.pop("is_last"      )
              UpperCamelCase       			=		model_inputs.pop("original_sizes"      ).tolist()
              UpperCamelCase       			=		model_inputs.pop("reshaped_input_sizes"      ).tolist()

              UpperCamelCase       			=		self.model(**UpperCAmelCase_      )

              # post processing happens here in order to avoid CPU GPU copies of ALL the masks
              UpperCamelCase       			=		model_outputs["pred_masks"]
              UpperCamelCase       			=		self.image_processor.post_process_masks(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   binarize=UpperCAmelCase_      )
              UpperCamelCase       			=		model_outputs["iou_scores"]
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.filter_masks(
                  masks[0]							,   iou_scores[0]							,   original_sizes[0]							,   input_boxes[0]							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   )
              return {
                  "masks": masks,
                  "is_last": is_last,
                  "boxes": boxes,
                  "iou_scores": iou_scores,
              }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				List[str]=False							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Optional[int]=0.7							,   )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              for model_output in model_outputs:
                     all_scores.append(model_output.pop("iou_scores"      )      )
                     all_masks.extend(model_output.pop("masks"      )      )
                     all_boxes.append(model_output.pop("boxes"      )      )

              UpperCamelCase       			=		torch.cat(UpperCAmelCase_      )
              UpperCamelCase       			=		torch.cat(UpperCAmelCase_      )
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.post_process_for_mask_generation(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		defaultdict(UpperCAmelCase_      )
              for output in model_outputs:
                     for k, v in output.items():
                            extra[k].append(UpperCAmelCase_      )

              UpperCamelCase       			=		{}
              if output_rle_mask:
                     UpperCamelCase       			=		rle_mask

              if output_bboxes_mask:
                     UpperCamelCase       			=		bounding_boxes

              return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





